Meta-recommendation of pork technological quality standards. (October 2021)

• Record Type: Journal Article
• Authors: Peres, Louise M.; Barbon Junior, Sylvio; Lopes, Jessica F.; Fuzyi, Estefânia M.; Barbon, Ana P.A.C.; Armangue, Joel G.; Bridi, Ana M.
• Is Part Of: Biosystems engineering. Volume 210(2021)
• Page Start: 13
• Page End: 19
• Publication Date: 2021-10
• Subjects: Classification -- Computational intelligence -- Decision tree -- Machine learning -- Meta-learning -- Pork quality; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2021.07.012
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Pork quality classification is supported by different reference standards that are widely reported in the literature. However, selecting the most suitable standard for each type of meat samples remains a challenge, due to their intrinsic variation according to the quality parameters' interval. The usage of meta-learning was proposed to automatically recommend the most adequate standard for a determined sample collection, leading to a more accurate classification. The meta-learning procedure has emerged from the machine learning research field to solve the algorithm selection dilemma, outlining a new method for pork quality classification. The applicability and advantages of using a suitable classification standard for pork quality were addressed using the J48 Decision Tree (DT) algorithm, which serves as the meta-recommender. Experiments conducted with six pork standards revealed promising results based on a few meta-attributes ( L ∗, water hold capacity, and dataset entropy) as the approach successfully recommended all scenarios. Highlights: Meta-learning usage in pork quality evaluations. L∗, WHC and dataset entropy were the most important meta-features. We provide a comprehensive identification of porks standards. Understanding of causes of higher incidence in poor meat quality categories.